SimDesign (version 0.4.1)

RMSE: Compute the (normalized) root mean square error

Description

Computes the average deviation (root mean square error; also known as the root mean square deviation) of a sample estimate from the population value. Accepts observed and population values, as well as observed values which are in deviation form.

Usage

RMSE(observed, population = NULL, type = "RMSE")

Arguments

observed

a numeric vector or matrix/data.frame of parameter estimates. If a vector, the length is equal to the number of replications. If a matrix/data.frame, the number of rows must equal the number of replications

population

a numeric scalar/vector indicating the fixed population values. If a single value is supplied and observed is a matrix/data.frame then the value will be recycled for each column. If NULL, then it will be assumed that the observed

type

type of deviation to compute. Can be 'RMSE' (default) for the root mean square-error, 'NRMSE' for the normalized RMSE (RMSE / (max(observed) - min(observed))), or 'CV' for the coefficient of variation

Value

• returns a numeric vector indicating the overall average deviation in the estimates

See Also

bias

MAE

Examples

pop <- 1
samp <- rnorm(100, 1, sd = 0.5)
RMSE(samp, pop)

dev <- samp - pop
RMSE(dev)

RMSE(samp, pop, type = 'NRMSE')
RMSE(dev, type = 'NRMSE')
RMSE(samp, pop, type = 'CV')

# matrix input
mat <- cbind(M1=rnorm(100, 2, sd = 0.5), M2 = rnorm(100, 2, sd = 1))
RMSE(mat, population = 2)

# same, but with data.frame
df <- data.frame(M1=rnorm(100, 2, sd = 0.5), M2 = rnorm(100, 2, sd = 1))
RMSE(df, population = c(2,2))